HEI – Page 2 – FAAI Job Hub

Article: Research and Analysis of IT Specifications of Good Practices in the Area of Artificial Intelligence

By admin | October 25, 2023 | Comments 0 Comment

This article is a contribution within the Erasmus+ project titled “The Future Lies in Applied Artificial Intelligence(FAAI) and examines research of collected IT specifications of good practices in Area of Artificial Intelligence (AAI). The article describes research conducted, the purpose of which is to find IT specifications of good practices in AI and describe their characteristics, like an area of implementation of the AI solution, the result of processing the data, the source of data, Data processing, and quality, what tools are used for processing data, and others. AAI application cases and the technologies used for implementation are reviewed. The specifics of the data and the applications used are described. The examination of these technologies will provide insight into which ones are favored and provide an overview of what is commonly referred to as “best practices” in this particular domain.The research encompassed a global examination of cases. The analysis of the data offers valuable insights in various directions:

 Application area of ML/AI

 Type of machine learning problems in described good

practices in Artificial Intelligence

 Type of models were developed within the projects

 What is the area of implementation of AI solution

 Used AI libraries (frameworks).

 Source of data

 Data characteristics

 Tools are used to store data

 What platform solution is used

 What type of storage is used.

The full paper can be found at: https://ieeexplore.ieee.org/document/10316145

Audio version is available:

Article: On Emerging Methodology for Collection of Good Practices in the Area of Applied Artificial Intelligence

By admin | October 25, 2023 | Comments 0 Comment

The work is fulfilled within the framework of Erasmus+ project “The Future is in Applied Artificial Intelligence” (FAAI) and devoted to the development the methodology for collecting and analyzing good practices in the field of applied artificial intelligence (AAI) regarding the competences, training, existing solutions and real cases, which can be used for developing training courses of competence based education. Here we propose the definition of good practice in the field of AAI together with the corresponding criteria and features. The offered methodology uses system research based on the data gathered from existing training courses in AAI, labor market, surveys filled in by academics, students and employers, AAI use cases in science and industry.

The full paper can be found at: https://ieeexplore.ieee.org/document/10316104

Audio version is available:

Article: Mathematical and Computer Simulation of the Response of a Potentiometric Biosensor for the Determination of α-сhaconine

By admin | August 30, 2023 | Comments 0 Comment

The article is devoted to the problem of developing a mathematical model of the response of a potentiometric biosensor for the determination of α-chaconine in the form of a system of seven differential equations that describe the dynamics of biochemical reactions during the full cycle of α-chaconine concentration measurement. At the same time, each of the differential equations establishes the concentration dependence of substrate, enzyme, inhibitor, enzyme-substrate, product, enzyme-inhibitor, enzyme-substrate-inhibitor complexes as a function of time. The mathematical model of the biosensor for the determination of α-chaconine was solved numerically in the R package. The input parameters of the system were used, namely, the concentrations of the enzyme, substrate, and inhibitor (5.8×10-4 M butyrylcholinesterase, 1×10-3 M butyrylcholine chloride, and 1×10−6; 2×10−6; 5×10−6; 10×10−6 M of α-chaconine, respectively), which are measured during experiments. To verify the model and compare it with the experimental response a potentiometric biosensor based on immobilized butyrylcholine chloride was used. Selection of direct and inverse rate constants of enzymatic reactions was carried out in such a way that the result of numerical modeling corresponded as much as possible to the experimental response of the studied biosensor. A comparative analysis of the experimental and simulated responses of the biosensor for the determination of αchaconine was established. It was found that the absolute error does not exceed 0.045 units. As a result of computer simullation, it was concluded that the developed kinetic model of the potentiometric biosensor makes it possible to identify all the main components that were measured this study.

The full paper can be found here: https://ceur-ws.org/Vol-3468/paper1.pdf

The audio version is available:

Article: Towards Resource-Efficient DNN Deployment for Traffic Object Recognition: From Edge to Fog

By admin | August 28, 2023 | Comments 0 Comment

The paper focuses on the challenges associated with deploying deep neural networks (DNNs) for the recognition of traffic objects using the camera of Android smartphones. The main objective of this research is to achieve resource-awareness, enabling efficient utilization of computational resources while maintaining high recognition accuracy. To achieve this, a methodology is proposed that leverages the Edge-to-Fog paradigm to distribute the inference workload across multiple tiers of the distributed system architecture. The evaluation was conducted using a dataset comprising real-world traffic scenarios and diverse traffic objects. The main findings of this research highlight the feasibility of deploying DNNs for traffic object recognition on resource-constrained Android smartphones. The proposed Edge-to-Fog methodology demonstrated improvements in terms of both recognition accuracy and resource utilization, and viability of both edge-only and edge-fog based approaches. Moreover, the experimental results showcased the adaptability of the system to dynamic traffic scenarios, thus ensuring real-time recognition performance even in challenging environments.

The link to conference can be found here: https://2023.euro-par.org/

The audio version is available:

Collecting real cases of AAI

By admin | August 26, 2023 | Comments 0 Comment

Project FAAI:2022-1-PL01-KA220-HED-000088359 “Future is in Applied Artificial Intelligence” (FAAI) under the Erasmus+ program started in September 2022. This project aims to bring together universities and business, and provide innovative solutions to develop artificial intelligence experts.

The project unites 5 partners from Central European and Eastern European universities: Poland, Slovakia, Serbia, Bulgaria and Montenegro.

In fulfillment of the goals set in the project, a case study with real application of AAI was conducted at stage WP2. The survey was conducted by the participants of this project.

Full paper can be found here: Research 8_EN_StateOfTheArt

Collecting IT specifications of good practices in AI

By admin | August 26, 2023 | Comments 0 Comment

The work presents the study of specifications of good practices in applied artificial intelligence (AAI). The analysis of 25 questionnaires from five partner institutions revealed key insights into the current state of artificial intelligence (AI) and machine learning (ML) projects. Training conducted in Serbia and Bulgaria, was signaling a need for expanded opportunities in EU countries. As a result of the study, we obtained that Deep ML prevails, particularly in Convolutional Neural Networks, while Gated Recurrent Unit is less common. Data volumes between 1 GB and 1 TB are typical, reflecting practical constraints. AI applications span diverse fields, with TensorFlow leading in libraries. Permissive licenses are most prevalent, databases are primary data sources, and texts/pictures dominate data characteristics. NoSQL databases are favored for storage. Security features and data processing tools vary. Dedicated servers and clusters are widely used, recommender systems are prominent, Python is the preferred language, and Apache Hadoop dominates ecosystems. Free datasets foster accessibility. Overall, the findings emphasize the dynamic nature of AI/ML projects, providing a foundation for future research in the rapidly advancing field.

Full paper can be found here: Research 7 StateOfTheArt_V3

Questionnaire for employers: Specifying graduate competencies in applied AI

By admin | August 26, 2023 | Comments 0 Comment

FAAI is an ERASMUS+ project that aims to evaluate existing AI systems and tools and develop common EU competencies for skill-building systems that use AI capabilities in the SME sector. The project aims to increase the quality and relevance of students’ and graduates’ knowledge and skills in AI/ML-specific topics based on skills needed in the labor market. This survey was conducted in the context of the FAAI project to assess the needs of employers in graduate competencies in Artificial Intelligence, Machine Learning, and Data Science in general. The survey aimed to research the needs and expectations of employers and companies for the purpose of training specialists in the field of Applied AI. A total of 38 companies filled in the survey representing a good starting point for examination and analysis of their needs related to applied AI. The survey consisted of 31 questions, including questions on general competencies needed, the type of machine learning problems solved, and the AI libraries used in companies. The survey also included questions on soft skills required, additional competencies needed, employer satisfaction with the level of preparation of Master’s studies’ graduates in the area of AI, and views towards raising the qualification of current employees of organizations by letting them study AI at а master’s level.

Full paper can be found here: Research 6

Questionnaire for IT Students, Masters and Alumni in Information Systems and Technologies

By admin | August 26, 2023 | Comments 0 Comment

This study is based on extensive survey conducted as a part of activities during the realization of the Erasmus+ project “Future is in Applied Artificial Intelligence”. Survey aimed to research the needs and expectations of IT graduates Masters and IT Alumni in Information Systems and Technologies regarding various Applied Artificial Intelligence topics with the goal to examine knowledge and attitude of students toward AI contents, current state of AI education and future directions of the transformation of education system toward competency-based education.

Full paper can be found here: Research 5

Survey for Academics (lecturers) in the field of applied AI

By admin | August 26, 2023 | Comments 0 Comment

The questionnaires of 80 teachers from 5 countries were collected and analyzed, concerning artificial intelligence teaching. Among the more interesting results belongs the finding, that most of the teachers are self-educated regarding artificial intelligence, the majority of them never participated in a commercial project regarding artificial intelligence, but most of the teachers would welcome extended participation of experts from industry in teaching of students. From their recommendations can be selected e.g. advices:

Focus more on the free versions.

Select proper computing language and libraries first
Attention on Computer Vision, Explainable AI, Human-AI interaction
Add more doing by examples activities
Solving real AI cases at classes

Most of the answers were analyzed and visualized in a form of graphs.

Full paper can ce found here: Research 4 StateOfTheArt_teachers_Dirgova

Survey of scientific projects in applied AI

By admin | August 26, 2023 | Comments 0 Comment

The questionnaires about 63 projects collected by partner organizations from the 5 countries were collected and analyzed, concerning artificial intelligence teaching. The project coordinators were from 19 countries. Among the more interesting results belongs the finding, that more than half of the projects concerned deep neural networks learning modules, and most machine learning tasks which were solved, were image processing, classification, regression, clusterization and natural language processing. Among the used AI libraries dominated TensorFlow, Keras, scikit-learn and CUDA. The programming languages were Python and C++.

Most of the answers were analyzed and visualized in a form of graphs.

Full document can be found here: Research 3 StateOfTheArt_scientificprojects_Dirgova